1. Field of the Invention
The present invention relates to a method for automatic analysis of failure or state messages generated by at least one complex system.
In particular, it relates to the analysis of failure and state messages generated by complex systems such as aircraft or automobiles. In the following description, only the case of aircraft is mentioned as an example, the described method and system being transposable to other types of vehicles.
2. Description of the Related Art
In the field of aeronautics, notably, maintenance of aircraft is of paramount importance for ensuring their reliability and minimizing the risk of accidents. This maintenance has several aspects. The aircraft are notably subject to regular, planned inspections, so as to ensure the absence of any malfunction. Moreover, inspections and repairs are also carried out when failures occur. On aircraft with a modern design, when one or more malfunctions occur during flight, these malfunctions are recorded by a system on board the aircraft in the form of failure or state messages. These messages are then analyzed on the ground by technicians with the purpose of determining the hardware problems, i.e. the components of the aircraft at the origin of the observed malfunctions and of repairing these components.
In order to facilitate these analyses, it is known how to resort to expert systems dedicated to the resolution of failures, which automatically infer from databases made up from solved failure cases and/or from theoretical failure cases stemming from fault tree modeling, which components and which failures may be at the origin of the recorded messages. Such a system is notably described in document US 2003/0167111 A1.
However, these expert systems are based on written reports on searches for failures and on formal fault trees, which do not always report the whole of the process for searching for failures and which may be inaccurate notably because of human errors and inhomogeneities in the writing of the reports and of the trees. Further, for complex apparatuses, the number of existing different failure or state messages, of the order of several thousands, makes the setting up of complete and reliable databases extremely complex, long and costly.
Moreover, such systems do not give the possibility of automatically distinguishing with self-learning, among the recorded messages, the messages which do not require analysis from the messages requiring extensive analysis, and for the latter such systems neither give the possibility of distinguishing the messages corresponding to known and documented failures or states from the non-documented messages.
Now, each use of a complex system such as an aircraft generates a large number of messages, often more than one thousand, and the absence of efficient and native filtering of these messages in the central maintenance computer slows down the diagnostic by the operator.